电测与仪表2024，Vol.61Issue(5)：160-165,6.DOI:10.19753/j.issn1001-1390.2024.05.022
改进BiLSTM在电力变压器故障诊断中的应用研究
Research on the application of improved BiLSTM in power transformer fault diagnosis
摘要
Abstract
In response to the problems of low diagnostic accuracy and inconsistent characteristic parameter stand-ards in current power transformer fault diagnosis methods,based on the analysis of power transformer faults,a fault diagnosis method of power transformer based on bidirectional short-term memory(BiLSTM)network and improved whale optimization algorithm is proposed.The hybrid strategy(weight and convergence factor optimization,bat al-gorithm and Levy flight strategy)is introduced to optimize the whale optimization algorithm,and the optimized whale optimization algorithm is used to find the optimal parameters of the bidirectional short-term memory network to establish the power transformer fault diagnosis model.The superiority of this method is verified through comparative analysis between numerical examples and conventional methods.The results show that compared to conventional methods,the proposed fault diagnosis method has higher fault diagnosis accuracy and the best practical application effect,the fault diagnosis accuracy has been improved by 10.42％ and 7.85％,providing a new approach for pow-er transformer fault diagnosis.关键词
电力变压器/故障诊断/双向长短期记忆网络/鲸鱼优化算法/混合策略Key words
power transformer/fault diagnosis/bidirectional long short-term memory/whale optimization algo-rithm/hybrid strategy分类
信息技术与安全科学引用本文复制引用
张寿岩,史卫刚,杨利国,裴跃辉,杨超..改进BiLSTM在电力变压器故障诊断中的应用研究[J].电测与仪表,2024,61(5):160-165,6.基金项目
河北省自然科学基金资助项目(F2021502013) （F2021502013）
